Many of today’s mobile software products and services, such as games, brands, social networks, or news feeds, need to engage their users in order to be successful, where engagement refers to the involvement into something that attracts and holds our attention.

Failing to engage users can endanger the sustainability of products and services, particularly if they are free to use and cover their costs through secondary streams of income, such as advertisements or upsells, which require repeated use of the service. However, engaging mobile users is increasingly challenging as we are exposed to an ever-growing number of online products and services which are all competing for our attention.

Many of today’s mobile products and services engage their users proactively via push notifications. However, such notifications are not always delivered at the right moment, therefore not meeting products’ and users’ expectations. Notifications delivered at the wrong moment or to uninterested users may even lead to churn.

To address this challenge, we aim at developing an intelligent mobile system that automatically and continually infers, whether a user would be open to engage with suggested content in each moment.

To inform the development of such a system, we carried out a field study with 337 mobile phone users. For 4 weeks, participants ran a study application on their primary phones. They were tasked to frequently report their current mood via a notification-administered experience-sampling questionnaire.

However, for this study, we were interested in whether they voluntarily engaged with content that we offered at the bottom of that questionnaire. In the informed consent, we had clearly communicated that interacting with this content is voluntary. Hence, our participants never felt required to interact with it.

For the prediction of whether participants would interact with this content, we used a wide range of data related to their mobile phone use, such as the time that the screen was last turned on, the current activity (walking, still, cycling, …), or the amount of data consumed during the last 60 minutes.

On the basis of 120 Million of such phone-use events and 78,930 questionnaire notifications, we build a machine-learning model that — just before delivering a questionnaire notification — predicts whether a participant will not only click on the notification, but also subsequently engage with the content offered at the bottom of the questionnaire.

When compared to a naïve baseline, which emulates current non-intelligent engagement strategies, our model achieves 66.6% higher success rate in its predictions. If the model also considers the user’s past behavior, predictions improve 5-fold over the baseline, while avoiding to failed engagement attempts with about one-third of the participants.

Such a classifier could be used in products to increase conversion rates, improve user experience, and lower churn by reducing the number of undesired interruptions.